Automated detection of choroidal neovascularization (cnv)

ABSTRACT

A method and system for detecting an advanced stage of age-related macular degeneration in a retina. Optical coherence tomography (OCT) imaging data for a retina is received. A presence of choroidal neovascularization (CNV) in the retina is detected, via a machine learning system, using the OCT imaging data. An output that indicates that the presence of CNV in the retina has been detected is generated.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of International ApplicationNo. PCT/US2021/061562, filed Dec. 2, 2021, which claims priority to andthe benefit of the U.S. Provisional Patent Application No. 63/121,723,filed Dec. 4, 2020, titled “Automated Detection Of ChoroidalNeovascularization (CNV),” which are hereby incorporated by reference intheir entirety as if fully set forth below and for all applicablepurposes.

FIELD

This application relates to the detection of choroidalneovascularization (CNV), and more particularly, to automated detectionof CNV in an eye using optical coherence tomography (OCT) imaging data.

INTRODUCTION

Age-related macular degeneration (AMD) is a leading cause of vision lossin subjects 50 years and older. Some subjects with AMD can developchoroidal neovascularization (CNV), in which new, abnormal blood vesselsoriginating in the choroid layer of the eye grow into the retina andleak fluid from the blood into the retina. Upon entering the retina, thefluid may distort the vision of a subject immediately. Over time, thefluid can damage the retina itself, for example, by causing the loss ofphotoreceptors in the retina. Unfortunately, there are currently no waysto anticipate the development of CNV. Accordingly, a desire exists forways to detect and track the progression of CNV.

SUMMARY

In one or more embodiments, a method is provided for detecting anadvanced stage of age-related macular degeneration. Optical coherencetomography (OCT) imaging data for a retina is received. A presence ofchoroidal neovascularization (CNV) in the retina is detected, via amachine learning system, using the OCT imaging data.

In one or more embodiments, a method is provided for detecting anadvanced stage of age-related macular degeneration. Optical coherencetomography (OCT) imaging data is received for a retina. The OCT imagingdata is preprocessed to form an OCT input. A presence of choroidalneovascularization (CNV) in the retina is detected, via a machinelearning system, using the OCT input.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the principles disclosed herein,and the advantages thereof, reference is now made to the followingdescriptions taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is a block diagram of a choroidal neovascularization (CNV)detection system in accordance with various embodiments.

FIG. 2 is a flowchart of a process for detecting an advanced stage ofage-related macular degeneration in a retina in accordance with variousembodiments.

FIG. 3 is a flowchart of another process for detecting an advanced stageof age-related macular degeneration in a retina in accordance withvarious embodiments.

FIG. 4 is a flowchart of another process for detecting an advanced stageof age-related macular degeneration in a retina in accordance withvarious embodiments.

FIG. 5 is a plot displaying a number of receiver operating curves (ROCs)based on a large number of test images from a number of subjects usingthe CNV detection system of FIG. 1 .

FIG. 6 is a block diagram of a computer system in accordance withvarious embodiments.

FIG. 7 illustrates an example neural network that can be used toimplement a deep learning neural network in accordance with variousembodiments.

It is to be understood that the figures are not necessarily drawn toscale, nor are the objects in the figures necessarily drawn to scale inrelationship to one another. The figures are depictions that areintended to bring clarity and understanding to various embodiments ofapparatuses, systems, and methods disclosed herein. Wherever possible,the same reference numbers will be used throughout the drawings to referto the same or like parts. Moreover, it should be appreciated that thedrawings are not intended to limit the scope of the present teachings inany way.

DETAILED DESCRIPTION I. OVERVIEW

Age-related macular degeneration (AMD) is an eye disease that can blur asubject's central vision and occurs when aging causes damage to portionsof the retina, such as the macula. AMD can be in dry form (oftenreferred to as atrophic AMD) or wet AMD (often referred to advancedneovascular AMD). Wet AMD occurs due to growth of abnormal blood vesselsin the eye. These new blood vessels, which originate from the choroid,often leak fluid (e.g., fluid from blood or red blood cells), wettingthe retina and eventually damaging portions of the retina, such as themacula. This condition is often referred to as choroidalneovascularization (CNV), which can result from, not only AMD, but alsoother causes such as, for example, pathologic myopia, ocularhistoplasmosis, eye trauma (e.g., angioid streaks), lacquer cracks, anduvetis (severe ocular inflammation).

Detecting CNV may be important to generating a personalized treatmentregimen for a subject, mitigating retinal damage, and understanding asubject's AMD pathogenesis. Detecting CNV can involve use of multiplemethods, including Optical Coherence Tomography (OCT), Fluorescein, ICGAngiography, and observed reduced visual acuity by a medicalprofessional OCT, in particular, is an imaging technique in which lightis directed at a biological sample (e.g., biological tissue) and thelight that is reflected from features of that biological sample iscollected to capture two- dimensional or three-dimensional,high-resolution cross-sectional images of the biological sample.

It is generally recognized that fluid associated with the retina (e.g.,intraretinal fluid, subretinal fluid, subretinal pigment epithelialfluid, etc.) may be an accurate and reliable indicator of CNV. Thus, amethodology for accurately and reliably detecting this fluid can serveas an accurate and reliable indicator of CNV. OCT images enablevisualizing such fluid and therefore, the presence of fluid in an OCTimage of a subject may be used as an indicator of CNV in the retina ofthe subject. However, manual analysis of OCT images by human graders maybe time-consuming and prone to error. Thus, methods and systems that canautomate the detection of CNV using OCT imaging data are desirable.

The present embodiments aim to leverage beneficial characteristics ofOCT imaging data in efforts to improved detection of CNV disease. Inparticular, the present embodiments include artificial intelligence(AI), and in particular machine learning and deep learning systems, thatautomatically analyze OCT imaging data and classify CNV disease based onthe analyzed OCT imaging data. These learning systems can classify CNVdisease at least as accurately as manual analysis, yet provide analysesmore quickly and more cost-effectively than current methods, especiallywhen processing large datasets. Moreover, analyses and diagnoses usinglearning systems enable the retaining of previous data within thelearning systems and a folding back in of such data to perpetuallyincrease the accuracy and speed of the automated CNV analysis and permitforward predictions of CNV progression. Thus, the methods and systems ofthe present disclosure allow for improved and automated analysis of OCTimaging data using a leaning system (e.g., a neural network system) forthe detection of CNV disease.

The present disclosure provides systems and methods of automateddetection of CNV disease in a patient eye using OCT imaging data. In oneembodiment, for example, the present disclosure provides systems andmethods of analyzing OCT imaging data using a neural network system.That is, for example, neural network systems may receive OCT imagingdata and generate CNV disease classification results that may be moreaccurate and informative compared to manual detection or even detectionusing static algorithms. This may be because using neural networksystems are trained to constantly optimize parameters of interest, thusimproving detection accuracy. Moreover, modules and/or blocks can beadded, removed or manipulated within the network to focus analysis tokey portions of the OCT imaging data, to incorporate loss function, toaugment OCT imaging data (though e.g., rotation, translation, andflipping), to reduce model size for e.g., speed, and automaticallyrecalibrate channel-wise feature response.

The neural network system may include but not limited to a convolutionalneural network (CNN) system, deep learning system (e.g., U-Net deeplearning neural network, Y-Net deep learning neural network, etc.),and/or the like. As discussed above, such systems and methods thatutilize neural network systems for analyzing OCT images facilitateaccurate detection of CNV disease while regularly allowing forconsistent improvement in the analysis accuracy over time throughreincorporation of said results back into the neural network system.

For example, OCT imaging data can be pre-processed, using a neuralnetwork system, by collapsing the data about a specific region ofinterest, such as the retinal pigment epithelium layer on the eye.Collapsing the OCT imaging data about a region of interest includescollapsing the OCT imaging data about a retinal pigment epithelium (RPE)layer of the patient eye, flattening one or more OCT images towards theRPE layer, and/or cropping a number of pixels about the RPE layer. This,combined with analysis of specific portions of the OCT imaging data(e.g., central B-scans), allows for increased focused analysis of theOCT data as well as reduced processing power needed for the analysis,therefore reducing analysis timing.

Recognizing and taking into account the importance and utility of amethodology and system that can provide the improvements describedabove, the specification describes various embodiments for automateddetection of CNV using OCT imaging data. More particularly, thespecification describes various embodiments of methods and systems foraccurately and reliably detecting CNV in a retina using OCT imaging dataand a machine learning system (e.g., a neural network system).

II. Definitions

The disclosure is not limited to these exemplary embodiments andapplications or to the manner in which the exemplary embodiments andapplications operate or are described herein. Moreover, the figures mayshow simplified or partial views, and the dimensions of elements in thefigures may be exaggerated or otherwise not in proportion.

In addition, as the terms “on,” “attached to,” “connected to,” “coupledto,” or similar words are used herein, one element (e.g., a component, amaterial, a layer, a substrate, etc.) can be “on,” “attached to,”“connected to,” or “coupled to” another element regardless of whetherthe one element is directly on, attached to, connected to, or coupled tothe other element or there are one or more intervening elements betweenthe one element and the other element. In addition, where reference ismade to a list of elements (e.g., elements a, b, c), such reference isintended to include any one of the listed elements by itself, anycombination of less than all of the listed elements, and/or acombination of all of the listed elements. Section divisions in thespecification are for ease of review only and do not limit anycombination of elements discussed.

The term “subject” may refer to a subject of a clinical trial, a personundergoing treatment, a person undergoing anti-cancer therapies, aperson being monitored for remission or recovery, a person undergoing apreventative health analysis (e.g., due to their medical history), orany other person or subject of interest. In various cases, “subject” and“subject” may be used interchangeably herein.

Unless otherwise defined, scientific and technical terms used inconnection with the present teachings described herein shall have themeanings that are commonly understood by those of ordinary skill in theart. Further, unless otherwise required by context, singular terms shallinclude pluralities and plural terms shall include the singular.Generally, nomenclatures utilized in connection with, and techniques of,chemistry, biochemistry, molecular biology, pharmacology and toxicologyare described herein are those well-known and commonly used in the art.

As used herein, “substantially” means sufficient to work for theintended purpose. The term “substantially” thus allows for minor,insignificant variations from an absolute or perfect state, dimension,measurement, result, or the like such as would be expected by a personof ordinary skill in the field but that do not appreciably affectoverall performance. When used with respect to numerical values orparameters or characteristics that can be expressed as numerical values,“substantially” means within ten percent.

As used herein, the term “about” used with respect to numerical valuesor parameters or characteristics that can be expressed as numericalvalues means within ten percent of the numerical values. For example,“about 50” means a value in the range from 45 to 55, inclusive.

The term “ones” means more than one.

As used herein, the term “plurality” can be 2, 3, 4, 5, 6, 7, 8, 9, 10,or more.

As used herein, the term “set of” means one or more. For example, a setof items includes one or more items.

As used herein, the phrase “at least one of,” when used with a list ofitems, means different combinations of one or more of the listed itemsmay be used and only one of the items in the list may be needed. Theitem may be a particular object, thing, step, operation, process, orcategory. In other words, “at least one of” means any combination ofitems or number of items may be used from the list, but not all of theitems in the list may be required. For example, without limitation, “atleast one of item A, item B, or item C” means item A; item A and item B;item B; item A, item B, and item C; item B and item C; or item A and C.In some cases, “at least one of item A, item B, or item C” means, but isnot limited to, two of item A, one of item B, and ten of item C; four ofitem B and seven of item C; or some other suitable combination.

As used herein, a “model” may include one or more algorithms, one ormore mathematical techniques, one or more machine learning algorithms,or a combination thereof.

As used herein, “machine learning” includes the practice of usingalgorithms to parse data, learn from it, and then make a determinationor prediction about something in the world. Machine learning usesalgorithms that can learn from data without relying on rules-basedprogramming.

As used herein, an “artificial neural network” or “neural network” (NN)may refer to mathematical algorithms or computational models that mimican interconnected group of artificial neurons that processes informationbased on a connectionistic approach to computation. Neural networks,which may also be referred to as neural nets, can employ one or morelayers of linear units, nonlinear units, or both to predict an outputfor a received input. Some neural networks include one or more hiddenlayers in addition to an output layer. The output of each hidden layermay be used as input to the next layer in the network, i.e., the nexthidden layer or the output layer. Each layer of the network may generatean output from a received input in accordance with current values of arespective set of parameters. In the various embodiments, a reference toa “neural network” may be a reference to one or more neural networks.

A neural network may process information in two ways; when it is beingtrained it is in training mode and when it puts what it has learned intopractice it is in inference (or prediction) mode. Neural networks maylearn through a feedback process (e.g., backpropagation) which allowsthe network to adjust the weight factors (modifying its behavior) of theindividual nodes in the intermediate hidden layers so that the outputmatches the outputs of the training data. In other words, a neuralnetwork learns by being provided with training data (learning examples)and eventually learns how to reach the correct output, even when it ispresented with a new range or set of inputs. A neural network mayinclude, for example, without limitation, at least one of a FeedforwardNeural Network (FNN), a Recurrent Neural Network (RNN), a Modular NeuralNetwork (MNN), a Convolutional Neural Network (CNN), a Residual NeuralNetwork (ResNet), an Ordinary Differential Equations Neural Networks(neural-ODE), a Squeeze and Excitation embedded neural network, aMobileNet, or another type of neural network.

As used herein, “deep learning” may refer to the use of multi-layeredartificial neural networks to automatically learn representations frominput data such as images, video, text, etc., without human providedknowledge, to deliver highly accurate predictions in tasks such asobject detection/identification, speech recognition, languagetranslation, etc.

As used herein, a “voxel” is a unit volume element (e.g., a volumetricpixel) of a regular grid of a three-dimensional entity with graphicinformation, such as a three-dimensional scan or image.

III. Automated Detection OF CNV

FIG. 1 is a block diagram of CNV evaluation system 100 in accordancewith various embodiments. CNV evaluation system 100 is used to detectCNV activity, including, for example, a presence of the CNV disease orits progression over time, in the retinas of subjects. The CNV diseasecan be characterized by a complex and progressive disease, such asneovascular age-related macular degeneration (nAMD). The CNV activitymay include growing of abnormal blood vessels originating in the choroidlayer of the eye into the retina and leaking fluid from the blood intothe retina. In such cases, the fluid that enters the retina may distortthe vision of a subject and damaging the retina itself, for example, bycausing the loss of photoreceptors in the retina.

As illustrated in FIG. 1 , CNV evaluation system 100 includes computingplatform 102, data storage 104, and display system 106. Computingplatform 102 may take various forms. In one or more embodiments,computing platform 102 includes a single computer (or computer system)or multiple computers in communication with each other. In otherexamples, computing platform 102 takes the form of a cloud computingplatform, a mobile computing platform (e.g., a smartphone, a tablet,etc.), or a combination thereof.

Data storage 104 and display system 106 are each in communication withcomputing platform 102. In some examples, data storage 104, displaysystem 106, or both may be considered part of or otherwise integratedwith computing platform 102. Thus, in some examples, computing platform102, data storage 104, and display system 106 may be separate componentsin communication with each other, but in other examples, somecombination of these components may be integrated together.

As illustrated in FIG. 1 , CNV evaluation system 100 includes imageprocessor 108, which may be implemented using hardware, software,firmware, or a combination thereof. In one or more embodiments, imageprocessor 108 is implemented in computing platform 102.

In various embodiments, a method for performing detection of CNV diseasecan be performed using CNV evaluation system 100, as described withrespect to FIG. 1 . The method can begin with inputting images forprocessing via CNV evaluation system 100 as follows. Image processor 108receives image input 109 for processing. In one or more embodiments,image input 109 includes OCT imaging data 110 for a retina of a subjector a person. The OCT imaging data 110 may include, for example, one ormore high-resolution OCT images of the retina. In one or moreembodiments, image input 109 includes images generated by a same imagingdevice or from multiple devices. In various embodiments, the OCT imagingdata 110 may include images of the same resolution or differentresolutions, same size or different sizes (e.g., in terms of pixels), orsame color depth or different color depths (e.g., 6-bit, 8-bit, 10-bit,12-bit, etc.). In one or more embodiments, any image of the OCT imagingdata 110 can be unregistered images. In other embodiments, any image ofthe OCT imaging data 110 may be registered images.

In various embodiments, image processor 108 processes image input 109using a CNV detection system 112, to detect the presence of CNV in theretina of the subject. The CNV detection system 112 may employ machinelearning with one or more neural networks based on the embodimentsdisclosed throughout the application. The one or more neural networksused in the disclosed method may include any number of or combination ofneural networks. In one or more embodiments, CNV detection system 112may include a convolutional neural network (CNN) system that includesone or more neural networks. In disclosed herein, one or more neuralnetworks of the CNV detection system 112 may include, for example,without limitation, at least one of a Feedforward Neural Network (FNN),a Recurrent Neural Network (RNN), a Modular Neural Network (MNN), aConvolutional Neural Network (CNN), a Residual Neural Network (ResNet),an Ordinary Differential Equations Neural Networks (neural-ODE), aSqueeze and Excitation embedded neural network, a MobileNet, or anothertype of neural network. In various embodiments, one or more neuralnetworks may be a deep learning neural network. In various embodiments,CNV detection system 112 may include at least one Squeeze and Excitation(SE) embedded neural network.

In one or more embodiments, image processor 108 generates output 114that indicates whether the presence of CNV in the retina has beendetected. For example, output 114 may be a probability value indicatingthe probability that CNV is present in the retina. The probability valuecan be anywhere from 0 to 100 and may be accompanied by a margin oferror or an uncertainty number (e.g., plus or minus values). In someexamples, output 114 may be a binary output that signals that CNV ispresent in the retina or that CNV is absent in the retina. In stillother examples, output 114 may be a value indicating an amount of fluidassociated with the retina that is detected from OCT imaging data 110.The value indicating the amount of fluid can be a numerical value,and/or may be accompanied by a measurement unit, such as, for example,nano-liter, micro-liter, etc. or any other suitable unit ofmeasurements.

FIG. 2 is a flowchart of a process 200 for detecting an advanced stageof age-related macular degeneration in a retina in accordance withvarious embodiments. This advanced stage may be, for example, choroidalneovascularization (CNV). In various embodiments, process 200 isimplemented using the CNV evaluation system 100 described with respectto FIG. 1 .

As illustrated in FIG. 2 , Step 202 includes receiving optical coherencetomography (OCT) imaging data for a retina of a patient. The OCT imagingdata may include one or more OCT images, such as those described withrespect to OCT imaging data 110 as described with respect to FIG. 1 ,and therefore, will not be described in further detail.

Step 204 includes detecting, via a machine learning system, a presenceof choroidal neovascularization (CNV) in the retina using the OCTimaging data. In one or more embodiments, step 204 is performed bydetecting a fluid associated with the retina. The fluid associated withthe retina can include, for example, but not limited to, intraretinalfluid, subretinal fluid, and subretinal pigment epithelial fluid. Themachine learning system used in detecting the presence of CNV mayinclude, for example, a neural network system. The neural network systemmay take the form of a deep learning neural network system. In someembodiments, the neural network system may include one or more of aFeedforward Neural Network (FNN), a Recurrent Neural Network (RNN), aModular Neural Network (MNN), a Convolutional Neural Network (CNN), aResidual Neural Network (ResNet), an Ordinary Differential EquationsNeural Networks (neural-ODE), a Squeeze and Excitation embedded neuralnetwork, a MobileNet, or another type of neural network. In variousembodiments, one or more neural networks may be a deep learning neuralnetwork. In various embodiments, one or more neural networks may includea Squeeze and Excitation (SE) embedded neural network.

In various embodiments, the neural network system may be trained on, forexample, both CNV-positive OCT images and CNV-negative OCT images. Insome embodiments, when the number of CNV-positive OCT images isdifferent from the number of CNV-negative OCT images, the neural networkmodel is trained with a weighted cross entropy loss function. Theweighted cross entropy loss function can be used when adjusting modelweights during training to minimize the loss between input and target.In various embodiments, the weighted cross entropy loss function ishelpful in case of unbalanced dataset (positive vs. negative) since themodel will pay more attention to the loss from the under-representedclass.

Step 206 includes generating an output that indicates that the presenceof CNV in the retina has been detected. This output, which may be output114 in FIG. 1 , may take various forms. For example, the output may be aprobability value indicating the probability that CNV activity ispresent within the retina. The probability value can be a numericalvalue ranging between 0 and 100. The probability value may beaccompanied by a margin of error or an uncertainty number. In variousembodiments, the output is a binary output that signals whether CNV ispresent or absent in the retina. In various embodiments, step 206 may beimplemented using the machine learning system as described in variousembodiments of the disclosure.

FIG. 3 is a flowchart of a process 300 for detecting an advanced stageof age-related macular degeneration in a retina in accordance withvarious embodiments. This advanced stage may be, for example, choroidalneovascularization (CNV). In various embodiments, process 300 isimplemented using the CNV evaluation system 100 described with respectto FIG. 1 .

Step 302 includes receiving optical coherence tomography (OCT) imagingdata for a retina of a patient. The OCT imaging data may include one ormore OCT images, such as those described with respect to OCT imagingdata 110 as described with respect to FIG. 1 , and therefore, will notbe described in further detail.

Once the OCT imaging data is received, process 300 proceeds to step 304,which includes preprocessing the OCT imaging data to form an OCT input.In some embodiments, the preprocessing of the OCT imaging data mayinclude selecting a portion of the OCT imaging data that captures aretinal pigment epithelium (RPE) layer of the retina as the OCT input.As one example, the preprocessing may include flattening an OCT imagetowards the RPE layer of the retina and selecting a portion of the OCTimage that includes the RPE layer of the retina, a first area above theRPE layer, and/or a second area below the RPE layer.

Step 306 includes detecting, via a machine learning system, a presenceof choroidal neovascularization (CNV) in the retina using the OCT input.The machine learning system used in detecting the presence of CNV mayinclude, for example, a neural network system. The neural network systemmay take the form of a deep learning neural network system. In someembodiments, the neural network system may include one or more of aFeedforward Neural Network (FNN), a Recurrent Neural Network (RNN), aModular Neural Network (MNN), a Convolutional Neural Network (CNN), aResidual Neural Network (ResNet), an Ordinary Differential EquationsNeural Networks (neural-ODE), a Squeeze and Excitation embedded neuralnetwork, a MobileNet, or another type of neural network. In variousembodiments, one or more neural networks may be a deep learning neuralnetwork. In various embodiments, one or more neural networks may includea Squeeze and Excitation (SE) embedded neural network.

In one or more embodiments, the machine learning system includes aneural network system that is trained to detect fluid in the retina,with the presence of the fluid indicating the presence of CNV. The fluidmay include, for example, but is not limited to, intraretinal fluid,subretinal fluid, subretinal pigment epithelial fluid, or a combinationthereof.

In one or more embodiments, an output may be generated based on thedetection of the presence of CNV in step 306. This output, which is thesame or substantially similar to output 114 in FIG. 1 , may take variousforms. For example, the output may be a probability value indicating theprobability that CNV activity is present within the retina. Theprobability value can be a numerical value ranging between 0 and 100.The probability value may be accompanied by a margin of error or anuncertainty number. In various embodiments, the output is a binaryoutput that signals whether CNV is present or absent in the retina.

FIG. 4 is a flowchart of an example process 400 in accordance withvarious embodiments. Process 400 can be used for detecting an advancedstage of age-related macular degeneration in a retina in accordance withvarious embodiments. This advanced stage may be, for example, choroidalneovascularization (CNV). In various embodiments, process 400 isimplemented using the CNV evaluation system 100 described with respectto FIG. 1 .

As illustrated, step 402 of process 400 includes receiving OCT imagingdata for a patient eye. The OCT imaging data may include one or more OCTimages, such as those described with respect to OCT imaging data 110 asdescribed with respect to FIG. 1 .

Once the OCT imaging data is received, process 400 proceeds to step 404,which includes collapsing the OCT imaging data about a region ofinterest, to form an OCT input. In various embodiments, collapsing theOCT imaging data includes collapsing the OCT imaging data about aretinal pigment epithelium (RPE) layer of the patient eye, to form theOCT input. In various embodiments, collapsing the OCT imaging dataincludes flattening one or more OCT images of the OCT imaging datatowards the RPE layer. In various embodiments, collapsing the OCTimaging data includes cropping a number of pixels about the RPE layer.

As illustrated in FIG. 4 , process 400 proceeds to step 406, whichincludes analyzing the OCT input for a presence of choroidalneovascularization (CNV) disease.

Once the OCT input is analyzed, process 400 proceeds to step 408, whichincludes detecting the presence of CNV disease in the patient eye usingthe OCT input. In various embodiments, detecting the presence of CNVdisease includes detecting retinal fluid in the patient eye using theOCT input, wherein the retinal fluid comprises at least one ofintraretinal fluid, subretinal fluid, or subretinal pigment epithelialfluid.

In various embodiments, detecting the presence of CNV disease in thepatient eye is performed using a machine learning system. In someembodiments, detecting the presence of CNV disease is performed using amachine learning system including a recalibration module. In someimplementations, detecting the presence of CNV disease is performed viaa squeeze and excitation module embedded on a machine learning system.In various implementations, the machine learning system used indetecting the presence of CNV may include, for example, a neural networksystem. The neural network system may take the form of a deep learningneural network system. In some embodiments, the neural network systemmay include one or more of a Feedforward Neural Network (FNN), aRecurrent Neural Network (RNN), a Modular Neural Network (MNN), aConvolutional Neural Network (CNN), a Residual Neural Network (ResNet),an Ordinary Differential Equations Neural Networks (neural-ODE), aSqueeze and Excitation embedded neural network, a MobileNet, or anothertype of neural network. In various embodiments, one or more neuralnetworks may be a deep learning neural network. In various embodiments,one or more neural networks may include a Squeeze and Excitation (SE)embedded neural network.

In one or more embodiments, the machine learning system includes aneural network system that is trained to detect fluid in the retina,with the presence of the fluid indicating the presence of CNV. The fluidmay include, for example, but is not limited to, intraretinal fluid,subretinal fluid, subretinal pigment epithelial fluid, or a combinationthereof.

In one or more embodiments, an output may be generated based on thedetection of the presence of CNV in step 406. This output generatedbased on the detection of step 406 is the same or substantially similarto output 114 in FIG. 1 , and thus may take various forms. In someembodiments, the output may be a probability value indicating theprobability that CNV activity is present within the retina. Theprobability value can be a numerical value ranging between 0 and 100.The probability value may be accompanied by a margin of error or anuncertainty number. In various embodiments, the output is a binaryoutput that signals whether CNV is present or absent in the patient eyeor retina of the patient eye.

FIG. 5 is a plot 500 displaying a number of receiver operating curves(ROCs) based on a large number of test images from a number of subjectsusing a CNV detection method or system such as, for example, the CNVevaluation system 100 of FIG. 1 . The plot 500 demonstrates that a deeplearning (DL) neural network (DL model with DL algorithms), such as thatused in the CNV detection system of FIG. 1 , can accurately detect a CNVdisease activity based on changes in the retinal anatomy on OCT imagingdata, for example OCT imaging data 110. Although the DL neural networkin generating the curves displayed in the plot 500, other neuralnetworks as described with respect to CNV detection system 112 can beused to generate the same or similar curves displayed in the plot 500.The DL model is evaluated on a test set of 1,706 images from 102subjects and achieved an Area Under Receiver Operating Curve (AUROC) of0.81±0.012, with accuracy of 0.76±0.027, sensitivity of 0.66±0.028, andspecificity of 0.83±0.029.

In generating the plot 500 of FIG. 5 , a total of 8,527 OCT images areevaluated. The OCT images are obtained from the pro re nata (PRN) armsof the HARBOR trial (clinical trial registration number: NCT00891735)that evaluated ranibizumab in neovascular age-related maculardegeneration (nAMD). Disease activity, for example, a presence or anabsence of CNV disease, in the study eye can be defined as fluid on OCT(e.g., intra-retinal fluid, subretinal fluid, or subretinal pigmentepithelial fluid) or if a subject's visual acuity decreased ≥5 lettersfrom the previous visit. In some instances, disease activity can also bedefined solely by the presence of retinal fluid associated withunderlying CNV on OCT without the requirement of a decrease of ≥5letters relative to the previous visit. In this subset of visits, studyeye OCT scans of 1024×512×128 voxels are collected from Zeiss Cirrusmachine and are flattened towards the retinal pigment epithelium (RPE)layer, and cropped to 384 pixels above and 128 below RPE. To accommodatethe graphical processing unit (GPU) memory constraints of the computersystem used for the study, the central 15 B-scans are selected asrepresentatives. As such, the input size of each scan to the network is512×512×15. A total of 8,527 scans from 521 subjects are used. 3,618 ofthem are from diseased eyes and 4,909 are without disease. They aresplit into training and test sets in a 4:1 ratio using stratifiedsampling. Five-fold cross validation is applied only using training setto optimize parameters. A Squeeze and Excitation (SE) embedded neuralnetwork model (with MobileNet) is designed to classify eyes with andwithout CNV disease. MobileNet greatly reduces a model size usingdepth-wise separable convolutions and SE module contributes byadaptively recalibrating channel-wise feature response. Weighted crossentropy is used as a loss function since the number of samples in eachclass are unbalanced. In some instances, data augmentation, includingrotation, translation, and/or flipping is applied during training of themodel/neural network.

In accordance with various embodiments disclosed herein, a method fordetecting a choroidal neovascularization (CNV) disease is described. Invarious embodiments, various process steps of the method can beperformed using a system, such as CNV evaluation system 100 of FIG. 1 .The method includes receiving optical coherence tomography (OCT) imagingdata for a patient eye; collapsing the OCT imaging data about a regionof interest, to form an OCT input; analyzing the OCT input for apresence of choroidal neovascularization (CNV) disease; and detectingthe presence of CNV disease in the patient eye using the OCT input.

In various embodiments of the method, detecting the presence of CNVdisease comprises detecting retinal fluid in the patient eye using theOCT input, wherein the retinal fluid comprises at least one ofintraretinal fluid, subretinal fluid, or subretinal pigment epithelialfluid.

In various embodiments of the method, collapsing the OCT imaging datacomprises collapsing the OCT imaging data about a retinal pigmentepithelium (RPE) layer of the patient eye, to form the OCT input. Invarious embodiments, collapsing the OCT imaging data comprisesflattening one or more OCT images of the OCT imaging data towards theRPE layer. In various embodiments, collapsing the OCT imaging datacomprises cropping a number of pixels about the RPE layer.

In various embodiments of the method, the detecting the presence of CNVdisease is performed using a machine learning system including arecalibration module. In various embodiments, the detecting the presenceof CNV disease is performed via a squeeze and excitation module embeddedon a machine learning system.

In accordance with various embodiments disclosed herein, automateddetection of choroidal neovascularization (CNV) disease can be performedan example system, such as CNV evaluation system 100 as described withrespect to FIG. 1 . In various embodiments, the system includes anon-transitory memory; and a hardware processor coupled with thenon-transitory memory and configured to read instructions from thenon-transitory memory to cause the system to perform operations. Forexample, CNV evaluation system 100 can be used to receive opticalcoherence tomography (OCT) imaging data, such as OCT imaging data 110,for a patient eye. Further, CNV evaluation system 100 of FIG. 1 can beused for processing. For example, image processor 108 of CNV evaluationsystem 100 can be used to process image input 109 using a machinelearning system, such as CNV detection system 112. Some non-limitingexamples of processing may include collapsing the OCT imaging data abouta region of interest, to form an OCT input.

In various embodiments, a neural network system, such as CNV detectionsystem 112 of CNV evaluation system 100, may be used for analyzing theOCT input for a presence of CNV disease and/or detecting the presence ofCNV disease in the patient eye using the OCT input. The neural networksystem used in analyzing the OCT input may include any number of orcombination of neural networks. In some embodiments, the neural networksystem may take the form of a convolutional neural network system thatincludes one or more neural networks. In some embodiments, neuralnetwork system may include any of a Feedforward Neural Network (FNN), aRecurrent Neural Network (RNN), a Modular Neural Network (MNN), aConvolutional Neural Network (CNN), a Residual Neural Network (ResNet),an Ordinary Differential Equations Neural Networks (neural-ODE), aSqueeze and Excitation embedded neural network, a MobileNet, or anothertype of neural network. In various embodiments, the neural networksystem may be a deep learning neural network or Squeeze and Excitation(SE) embedded neural network.

In various embodiments of the system, detecting the presence of CNVdisease comprises detecting retinal fluid in the patient eye using theOCT input, wherein the retinal fluid comprises at least one ofintraretinal fluid, subretinal fluid, or subretinal pigment epithelialfluid. In various embodiments of the system, collapsing the OCT imagingdata comprises collapsing the OCT imaging data about the patient'sretinal pigment epithelium (RPE) layer, to form the OCT input. Invarious embodiments of the system, collapsing the OCT imaging datacomprises flattening OCT images of the OCT imaging data towards the RPElayer. In various embodiments of the system, collapsing the OCT imagingdata comprises cropping a number of pixels about the RPE layer.

In various embodiments of the system, the machine learning system usedfor detecting the presence of CNV disease may include a recalibrationmodule. In various embodiments, the detecting the presence of CNVdisease is performed via a squeeze and excitation module embedded on amachine learning system.

In accordance with various embodiments disclosed herein, anon-transitory computer-readable medium having stored thereoncomputer-readable instructions is described. The non-transitorycomputer-readable medium includes computer-readable instructionsexecutable to cause a computer system to perform operations. In variousembodiments, the operations can be performed using a CNV detectionsystem such as, for example, CNV evaluation system 100 as described withrespect to FIG. 1 . The computer-readable instructions of the operationsincludes: receiving optical coherence tomography (OCT) imaging data fora patient eye; collapsing the OCT imaging data about a region ofinterest, to form an OCT input; analyzing the OCT input for a presenceof CNV disease; and detecting the presence of CNV disease in the patienteye using the OCT input.

In various embodiments, detecting the presence of CNV disease comprisesdetecting retinal fluid in the patient eye using the OCT input, whereinthe retinal fluid comprises at least one of intraretinal fluid,subretinal fluid, or subretinal pigment epithelial fluid.

In various embodiments, collapsing the OCT imaging data includescollapsing the OCT imaging data about the patient's retinal pigmentepithelium (RPE) layer, to form the OCT input. In various embodiments,collapsing the OCT imaging data comprises flattening OCT images of theOCT imaging data towards the RPE layer. In various embodiments,collapsing the OCT imaging data comprises cropping a number of pixelsabout the RPE layer. In various embodiments, the detecting the presenceof CNV disease is performed via a squeeze and excitation module embeddedon a machine learning system.

IV. Computer Implemented System

FIG. 6 is a block diagram of a computer system in accordance withvarious embodiments. Computer system 600 may be an example of oneimplementation for computing platform 102 described above in FIG. 1 . Inone or more examples, computer system 600 can include a bus 602 or othercommunication mechanism for communicating information, and a processor604 coupled with bus 602 for processing information. In variousembodiments, computer system 600 can also include a memory, which can bea random-access memory (RAM) 606 or other dynamic storage device,coupled to bus 602 for determining instructions to be executed byprocessor 604. Memory also can be used for storing temporary variablesor other intermediate information during execution of instructions to beexecuted by processor 604. In various embodiments, computer system 600can further include a read only memory (ROM) 608 or other static storagedevice coupled to bus 602 for storing static information andinstructions for processor 604. A storage device 610, such as a magneticdisk or optical disk, can be provided and coupled to bus 602 for storinginformation and instructions.

In various embodiments, computer system 600 can be coupled via bus 602to a display 612, such as a cathode ray tube (CRT) or liquid crystaldisplay (LCD), for displaying information to a computer user. An inputdevice 614, including alphanumeric and other keys, can be coupled to bus602 for communicating information and command selections to processor604. Another type of user input device is a cursor control 616, such asa mouse, a joystick, a trackball, a gesture input device, a gaze-basedinput device, or cursor direction keys for communicating directioninformation and command selections to processor 604 and for controllingcursor movement on display 612. This input device 614 typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane.However, it should be understood that input devices 614 allowing forthree-dimensional (e.g., x, y and z) cursor movement are alsocontemplated herein.

Consistent with certain implementations of the present teachings,results can be provided by computer system 600 in response to processor604 executing one or more sequences of one or more instructionscontained in RAM 606. Such instructions can be read into RAM 606 fromanother computer-readable medium or computer-readable storage medium,such as storage device 610. Execution of the sequences of instructionscontained in RAM 606 can cause processor 604 to perform the processesdescribed herein. Alternatively, hard-wired circuitry can be used inplace of or in combination with software instructions to implement thepresent teachings. Thus, implementations of the present teachings arenot limited to any specific combination of hardware circuitry andsoftware.

The term “computer-readable medium” (e.g., data store, data storage,storage device, data storage device, etc.) or “computer-readable storagemedium” as used herein refers to any media that participates inproviding instructions to processor 604 for execution. Such a medium cantake many forms, including but not limited to, non-volatile media,volatile media, and transmission media. Examples of non-volatile mediacan include, but are not limited to, optical, solid state, magneticdisks, such as storage device 610. Examples of volatile media caninclude, but are not limited to, dynamic memory, such as RAM 606.Examples of transmission media can include, but are not limited to,coaxial cables, copper wire, and fiber optics, including the wires thatcomprise bus 602.

Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, a RAM, PROM, and EPROM, aFLASH-EPROM, any other memory chip or cartridge, or any other tangiblemedium from which a computer can read.

In addition to computer readable medium, instructions or data can beprovided as signals on transmission media included in a communicationsapparatus or system to provide sequences of one or more instructions toprocessor 604 of computer system 600 for execution. For example, acommunication apparatus may include a transceiver having signalsindicative of instructions and data. The instructions and data areconfigured to cause one or more processors to implement the functionsoutlined in the disclosure herein. Representative examples of datacommunications transmission connections can include, but are not limitedto, telephone modem connections, wide area networks (WAN), local areanetworks (LAN), infrared data connections, NFC connections, opticalcommunications connections, etc.

It should be appreciated that the methodologies described herein, flowcharts, diagrams, and accompanying disclosure can be implemented usingcomputer system 600 as a standalone device or on a distributed networkof shared computer processing resources such as a cloud computingnetwork.

The methodologies described herein may be implemented by various meansdepending upon the application. For example, these methodologies may beimplemented in hardware, firmware, software, or any combination thereof.For a hardware implementation, the processing unit may be implementedwithin one or more application specific integrated circuits (ASICs),digital signal processors (DSPs), digital signal processing devices(DSPDs), programmable logic devices (PLDs), field programmable gatearrays (FPGAs), processors, controllers, micro-controllers,microprocessors, electronic devices, other electronic units designed toperform the functions described herein, or a combination thereof.

In various embodiments, the methods of the present teachings may beimplemented as firmware and/or a software program and applicationswritten in conventional programming languages such as C, C++, Python,etc. If implemented as firmware and/or software, the embodimentsdescribed herein can be implemented on a non-transitorycomputer-readable medium in which a program is stored for causing acomputer to perform the methods described above. It should be understoodthat the various engines described herein can be provided on a computersystem, such as computer system 600, whereby processor 604 would executethe analyses and determinations provided by these engines, subject toinstructions provided by any one of, or a combination of, the memorycomponents RAM 606, ROM, 608, or storage device 610 and user inputprovided via input device 614.

V. Artificial Neural Networks

FIG. 7 illustrates an example neural network that can be used toimplement a computer-based model according to various embodiments of thepresent disclosure. For example, the neural network 700 may be used toimplement the CNV detection system 112 of the CNV evaluation system 100.As shown, the artificial neural network 700 includes three layers—aninput layer 702, a hidden layer 704, and an output layer 706. Each ofthe layers 702, 704, and 706 may include one or more nodes. For example,the input layer 702 includes nodes 708-714, the hidden layer 704includes nodes 716-718, and the output layer 706 includes a node 722. Inthis example, each node in a layer is connected to every node in anadjacent layer. For example, the node 708 in the input layer 702 isconnected to both of the nodes 716, 718 in the hidden layer 704.Similarly, the node 716 in the hidden layer is connected to all of thenodes 708-714 in the input layer 702 and the node 722 in the outputlayer 706. Although only one hidden layer is shown for the artificialneural network 700, it has been contemplated that the artificial neuralnetwork 700 used to implement a neural network system, such as the CNVdetection system 112 of the CNV evaluation system 100, may include asmany hidden layers as necessary or desired.

In this example, the artificial neural network 700 receives a set ofinput values (inputs 1-4) and produces an output value (output 5). Eachnode in the input layer 702 may correspond to a distinct input value.For example, when the artificial neural network 700 is used to implementa neural network system, such as the CNV detection system 112 of the CNVevaluation system 100, each node in the input layer 702 may correspondto a distinct attribute of the OCT imaging data 110.

In some embodiments, each of the nodes 716-718 in the hidden layer 704generates a representation, which may include a mathematical computation(or algorithm) that produces a value based on the input values receivedfrom the nodes 708-714. The mathematical computation may includeassigning different weights to each of the data values received from thenodes 708-714. The nodes 716 and 718 may include different algorithmsand/or different weights assigned to the data variables from the nodes708-714 such that each of the nodes 716-718 may produce a differentvalue based on the same input values received from the nodes 708-714. Insome embodiments, the weights that are initially assigned to thefeatures (or input values) for each of the nodes 716-718 may be randomlygenerated (e.g., using a computer randomizer). The values generated bythe nodes 716 and 718 may be used by the node 722 in the output layer706 to produce an output value for the artificial neural network 700.When the artificial neural network 700 is used to implement a neuralnetwork system, such as the CNV detection system 112 of the CNVevaluation system 100, the output value produced by the artificialneural network 700 may include the output 114.

The artificial neural network 700 may be trained by using training data.For example, the training data herein may be a set of images from OCTimaging data 110. By providing training data to the artificial neuralnetwork 700, the nodes 716-718 in the hidden layer 704 may be trained(adjusted) such that an optimal output is produced in the output layer706 based on the training data. By continuously providing different setsof training data, and penalizing the artificial neural network 700 whenthe output of the artificial neural network 700 is incorrect (e.g., whengenerating segmentation masks including incorrect GA lesion segments),the artificial neural network 700 (and specifically, the representationsof the nodes in the hidden layer 704) may be trained (adjusted) toimprove its performance in data classification. Adjusting the artificialneural network 700 may include adjusting the weights associated witheach node in the hidden layer 704.

Although the above discussions pertain to an artificial neural networkas an example of machine learning, it is understood that other types ofmachine learning methods may also be suitable to implement the variousaspects of the present disclosure. For example, support vector machines(SVMs) may be used to implement machine learning. SVMs are a set ofrelated supervised learning methods used for classification andregression. A SVM training algorithm—which may be a non-probabilisticbinary linear classifier—may build a model that predicts whether a newexample falls into one category or another. As another example, Bayesiannetworks may be used to implement machine learning. A Bayesian networkis an acyclic probabilistic graphical model that represents a set ofrandom variables and their conditional independence with a directedacyclic graph (DAG). The Bayesian network could present theprobabilistic relationship between one variable and another variable.Another example is a machine learning engine that employs a decisiontree learning model to conduct the machine learning process. In someinstances, decision tree learning models may include classification treemodels, as well as regression tree models. In some embodiments, themachine learning engine employs a Gradient Boosting Machine (GBM) model(e.g., XGBoost) as a regression tree model. Other machine learningtechniques may be used to implement the machine learning engine, forexample via Random Forest or Deep Neural Networks. Other types ofmachine learning algorithms are not discussed in detail herein forreasons of simplicity and it is understood that the present disclosureis not limited to a particular type of machine learning.

VI. Conclusion

While the present teachings are described in conjunction with variousembodiments, it is not intended that the present teachings be limited tosuch embodiments. On the contrary, the present teachings encompassvarious alternatives, modifications, and equivalents, as will beappreciated by those of skill in the art.

For example, the flowcharts and block diagrams described aboveillustrate the architecture, functionality, and/or operation of possibleimplementations of various method and system embodiments. Each block inthe flowcharts or block diagrams may represent a module, a segment, afunction, a portion of an operation or step, or a combination thereof.In some alternative implementations of an embodiment, the function orfunctions noted in the blocks may occur out of the order noted in thefigures. For example, in some cases, two blocks shown in succession maybe executed substantially concurrently. In other cases, the blocks maybe performed in the reverse order. Further, in some cases, one or moreblocks may be added to replace or supplement one or more other blocks ina flowchart or block diagram.

Thus, in describing the various embodiments, the specification may havepresented a method and/or process as a particular sequence of steps.However, to the extent that the method or process does not rely on theparticular order of steps set forth herein, the method or process shouldnot be limited to the particular sequence of steps described, and oneskilled in the art can readily appreciate that the sequences may bevaried and still remain within the spirit and scope of the variousembodiments.

VII. Recitation of Embodiments

Embodiment 1: A method comprising: receiving optical coherencetomography (OCT) imaging data for a patient eye; collapsing the OCTimaging data about a region of interest, to form an OCT input; analyzingthe OCT input for a presence of choroidal neovascularization (CNV)disease; and detecting the presence of CNV disease in the patient eyeusing the OCT input.

Embodiment 2: The method of Embodiment 1, wherein detecting the presenceof CNV disease comprises detecting retinal fluid in the patient eyeusing the OCT input, wherein the retinal fluid comprises at least one ofintraretinal fluid, subretinal fluid, or subretinal pigment epithelialfluid.

Embodiment 3: The method of Embodiments 1 or 2, wherein collapsing theOCT imaging data comprises collapsing the OCT imaging data about aretinal pigment epithelium (RPE) layer of the patient eye, to form theOCT input.

Embodiment 4: The method of Embodiment 3, wherein collapsing the OCTimaging data comprises flattening one or more OCT images of the OCTimaging data towards the RPE layer.

Embodiment 5: The method of Embodiments 3 or 4, wherein collapsing theOCT imaging data comprises cropping a number of pixels about the RPElayer.

Embodiment 6: The method of any of Embodiments 1-5, wherein thedetecting the presence of CNV disease is performed using a machinelearning system including a recalibration module.

Embodiment 7: The method of any of Embodiments 1-6, wherein thedetecting the presence of CNV disease is performed via a squeeze andexcitation module embedded on a machine learning system.

Embodiment 8: A system, comprising: a non-transitory memory; and ahardware processor coupled with the non-transitory memory and configuredto read instructions from the non-transitory memory to cause the systemto perform operations comprising: receiving optical coherence tomography(OCT) imaging data for a patient eye; collapsing the OCT imaging dataabout a region of interest, to form an OCT input; analyzing the OCTinput for a presence of CNV disease; and detecting the presence of CNVdisease in the patient eye using the OCT input.

Embodiment 9: The system of Embodiment 8, wherein detecting the presenceof CNV disease comprises detecting retinal fluid in the patient eyeusing the OCT input, wherein the retinal fluid comprises at least one ofintraretinal fluid, subretinal fluid, or subretinal pigment epithelialfluid.

Embodiment 10: The system of Embodiments 8 or 9, wherein collapsing theOCT imaging data comprises collapsing the OCT imaging data about thepatient's retinal pigment epithelium (RPE) layer, to form the OCT input.

Embodiment 11: The system of Embodiment 10, wherein collapsing the OCTimaging data comprises flattening OCT images of the OCT imaging datatowards the RPE layer.

Embodiment 12: The system of Embodiments 10 or 11, wherein collapsingthe OCT imaging data comprises cropping a number of pixels about the RPElayer.

Embodiment 13: The system of any of Embodiments 8-12, wherein thedetecting the presence of CNV disease is performed using a machinelearning system including a recalibration module.

Embodiment 14: The system of any of Embodiments 8-13, wherein thedetecting the presence of CNV disease is performed via a squeeze andexcitation module embedded on a machine learning system.

Embodiment 15: A non-transitory computer-readable medium having storedthereon computer-readable instructions executable to cause a computersystem to perform operations comprising: receiving optical coherencetomography (OCT) imaging data for a patient eye; collapsing the OCTimaging data about a region of interest, to form an OCT input; analyzingthe OCT input for a presence of CNV disease; and detecting the presenceof CNV disease in the patient eye using the OCT input.

Embodiment 16: The non-transitory computer-readable medium of Embodiment15, wherein detecting the presence of CNV disease comprises detectingretinal fluid in the patient eye using the OCT input, wherein theretinal fluid comprises at least one of intraretinal fluid, subretinalfluid, or subretinal pigment epithelial fluid.

Embodiment 17: The non-transitory computer-readable medium ofEmbodiments 15 or 16, wherein collapsing the OCT imaging data comprisescollapsing the OCT imaging data about the patient's retinal pigmentepithelium (RPE) layer, to form the OCT input.

Embodiment 18: The non-transitory computer-readable medium of Embodiment17, wherein collapsing the OCT imaging data comprises flattening OCTimages of the OCT imaging data towards the RPE layer.

Embodiment 19: The non-transitory computer-readable medium of any ofEmbodiments 15-18, wherein collapsing the OCT imaging data comprisescropping a number of pixels about the RPE layer.

Embodiment 20: The non-transitory computer-readable medium of any ofEmbodiments 15-19, wherein the detecting the presence of CNV disease isperformed via a squeeze and excitation module embedded on a machinelearning system.

1. A method comprising: receiving optical coherence tomography (OCT)imaging data for a patient eye; collapsing the OCT imaging data about aregion of interest, to form an OCT input; analyzing the OCT input for apresence of choroidal neovascularization (CNV) disease; and detectingthe presence of CNV disease in the patient eye using the OCT input. 2.The method of claim 1, wherein detecting the presence of CNV diseasecomprises detecting retinal fluid in the patient eye using the OCTinput, wherein the retinal fluid comprises at least one of intraretinalfluid, subretinal fluid, or subretinal pigment epithelial fluid.
 3. Themethod of claim 1, wherein collapsing the OCT imaging data comprisescollapsing the OCT imaging data about a retinal pigment epithelium (RPE)layer of the patient eye, to form the OCT input.
 4. The method of claim3, wherein collapsing the OCT imaging data comprises flattening one ormore OCT images of the OCT imaging data towards the RPE layer.
 5. Themethod of claim 3, wherein collapsing the OCT imaging data comprisescropping a number of pixels about the RPE layer.
 6. The method of claim1, wherein the detecting the presence of CNV disease is performed usinga machine learning system including a recalibration module.
 7. Themethod of claim 1, wherein the detecting the presence of CNV disease isperformed via a squeeze and excitation module embedded on a machinelearning system.
 8. A system, comprising: a non-transitory memory; and ahardware processor coupled with the non-transitory memory and configuredto read instructions from the non-transitory memory to cause the systemto perform operations comprising: receiving optical coherence tomography(OCT) imaging data for a patient eye; collapsing the OCT imaging dataabout a region of interest, to form an OCT input; analyzing the OCTinput for a presence of CNV disease; and detecting the presence of CNVdisease in the patient eye using the OCT input.
 9. The system of claim8, wherein detecting the presence of CNV disease comprises detectingretinal fluid in the patient eye using the OCT input, wherein theretinal fluid comprises at least one of intraretinal fluid, subretinalfluid, or subretinal pigment epithelial fluid.
 10. The system of claim8, wherein collapsing the OCT imaging data comprises collapsing the OCTimaging data about the patient's retinal pigment epithelium (RPE) layer,to form the OCT input.
 11. The system of claim 10, wherein collapsingthe OCT imaging data comprises flattening OCT images of the OCT imagingdata towards the RPE layer.
 12. The system of claim 10, whereincollapsing the OCT imaging data comprises cropping a number of pixelsabout the RPE layer.
 13. The system of claim 8, wherein the detectingthe presence of CNV disease is performed using a machine learning systemincluding a recalibration module.
 14. The system of claim 8, wherein thedetecting the presence of CNV disease is performed via a squeeze andexcitation module embedded on a machine learning system.
 15. Anon-transitory computer-readable medium having stored thereoncomputer-readable instructions executable to cause a computer system toperform operations comprising: receiving optical coherence tomography(OCT) imaging data for a patient eye; collapsing the OCT imaging dataabout a region of interest, to form an OCT input; analyzing the OCTinput for a presence of CNV disease; and detecting the presence of CNVdisease in the patient eye using the OCT input.
 16. The non-transitorycomputer-readable medium of claim 15, wherein detecting the presence ofCNV disease comprises detecting retinal fluid in the patient eye usingthe OCT input, wherein the retinal fluid comprises at least one ofintraretinal fluid, subretinal fluid, or subretinal pigment epithelialfluid.
 17. The non-transitory computer-readable medium of claim 15,wherein collapsing the OCT imaging data comprises collapsing the OCTimaging data about the patient's retinal pigment epithelium (RPE) layer,to form the OCT input.
 18. The non-transitory computer-readable mediumof claim 17, wherein collapsing the OCT imaging data comprisesflattening OCT images of the OCT imaging data towards the RPE layer. 19.The non-transitory computer-readable medium of claim 17, whereincollapsing the OCT imaging data comprises cropping a number of pixelsabout the RPE layer.
 20. The non-transitory computer-readable medium ofclaim 15, wherein the detecting the presence of CNV disease is performedvia a squeeze and excitation module embedded on a machine learningsystem.